1.2. What are Scenarios?

Scenarios are images of the future, or alternative futures. They are neither
predictions nor forecasts. Rather, each scenario is one alternative image of
how the future might unfold. A set of scenarios assists in the understanding
of possible future developments of complex systems. Some systems, those that
are well understood and for which complete information is available, can be
modeled with some certainty, as is frequently the case in the physical sciences,
and their future states predicted. However, many physical and social systems
are poorly understood, and information on the relevant variables is so incomplete
that they can be appreciated only through intuition and are best communicated
by images and stories. Prediction is not possible in such cases (see Box
1-1 on uncertainties inherent in scenario analysis).

Box 1-1: Uncertainties and Scenario Analysis

In general, there are three types of uncertainty: uncertainty in quantities,
uncertainty about model structure and uncertainties that arise from disagreements
among experts about the value of quantities or the functional form of
the model (Morgan and Henrion, 1990). Sources of uncertainty could be
statistical variation, subjective judgment (systematic error), imperfect
definition (linguistic imprecision), natural variability, disagreement
among experts and approximation (Morgan and Henrion, 1990). Others (Funtowicz
and Ravetz, 1990) distinguish three main sources of uncertainty: "data
uncertainties,""modeling uncertainties" and "completeness uncertainties."
Data uncertainties arise from the quality or appropriateness of the data
used as inputs to models. Modeling uncertainties arise from an incomplete
understanding of the modeled phenomena, or from approximations that are
used in formal representation of the processes. Completeness uncertainties
refer to all omissions due to lack of knowledge. They are, in principle,
non-quantifiable and irreducible.

Scenarios help in the assessment of future developments in complex systems
that are either inherently unpredictable, or that have high scientific
uncertainties. In all stages of the scenario-building process, uncertainties
of different nature are encountered. A large uncertainty surrounds future
emissions and the possible evolution of their underlying driving forces,
as reflected in a wide range of future emissions paths in the literature.
The uncertainty is further compounded in going from emissions paths to
climate change, from climate change to possible impacts and finally from
these driving forces to formulating adaptation and mitigation measures
and policies. The uncertainties range from inadequate scientific understanding
of the problems, data gaps and general lack of data to inherent uncertainties
of future events in general. Hence the use of alternative scenarios to
describe the range of possible future emissions.

For the current SRES scenarios, the following sources of uncertainties
are identified: Choice of Storylines. Freedom in choice of qualitative scenario
parameter combinations, such as low population combined with high gross
domestic product (GDP), contributes to scenario uncertainty. Authors Interpretation of Storylines. Uncertainty in the individual
modeler's translation of narrative scenario storyline text in quantitative
scenario drivers. Two kinds of parameters can be distinguished:

Harmonized drivers such as population, GDP, and final energy (see
Section 4.1. in Chapter 4). Inter-scenario uncertainty
is reduced in the harmonized runs as the modeling teams decided to keep
population and GDP within certain agreed boundaries.

Other assumed parameters were chosen freely by the modelers, consistent
with the storylines.

Translation of the Understanding of Linkages between Driving Forces
into Quantitative Inputs for Scenario Analysis. Often the understanding
of the linkages is incomplete or qualitative only. This makes it difficult
for modelers to implement these linkages in a consistent manner. Methodological Differences.

Uncertainty induced by conceptual and structural differences in the
way models work (model approaches) and in the ways models are parameterized.

Uncertainty in the assumptions that underlie the relationships between
scenario drivers and output, such as the relationship between average
income and diet change.

Different Sources of Data. Data differ from a variety of well-acknowledged
scientific studies, since "measurements" always provide ranges and not
exact values. Therefore, modelers can only choose from ranges of input
parameters for. For example:

Base year data.

Historical development trajectories.

Current investment requirements.

Inherent Uncertainties. These uncertainties stem from the fact
that unexpected "rare" events or events that a majority of researchers
currently consider to be "rare future events" might nevertheless occur
and produce outcomes that are fundamentally different from those produced
by SRES model runs.

Scenarios can be viewed as a linking tool that integrates qualitative narratives
or stories about the future and quantitative formulations based on formal modeling.
As such they enhance our understanding of how systems work, behave and evolve.
Scenarios are useful tools for scientific assessments, for learning about complex
systems behavior and for policy making (Jefferson, 1983; Davis, 1999). In scientific
assessments, scenarios are usually based on an internally consistent and reproducible
set of assumptions or theories about the key relationships and driving forces
of change, which are derived from our understanding of both history and the
current situation. Often scenarios are formulated with the help of numeric or
analytic formal models.

Future levels of global GHG emissions are the products of a very complex, ill-understood
dynamic system, driven by forces such as population growth, socio-economic development,
and technological progress; thus to predict emissions accurately is virtually
impossible. However, near-term policies may have profound long-term climate
impacts. Consequently, policy-makers need a summary of what is understood about
possible future GHG emissions, and given the uncertainties in both emissions
models and our understanding of key driving forces, scenarios are an appropriate
tool for summarizing both current understanding and current uncertainties. For
such scenarios to be useful for climate models, impact assessments and the design
of mitigation and adaptation policies, both the main outputs of the SRES scenarios
(emissions) and the main inputs or driving forces (population growth, economic
growth, technological, e.g., as it affects energy and land-use) are equally
important.

GHG emissions scenarios are usually based on an internally consistent and reproducible
set of assumptions about the key relationships and driving forces of change,
which are derived from our understanding of both history and the current situation.
Often these scenarios are formulated with the help of formal models. Such scenarios
specify the future emissions of GHGs in quantitative terms and, if fully documented,
they are also reproducible. Sometimes GHG emissions scenarios are less quantitative
and more descriptive, and in a few cases they do not involve any formal analysis
and are expressed in qualitative terms. The SRES scenarios involve both qualitative
and quantitative components; they have a narrative part called "storylines"
and a number of corresponding quantitative scenarios for each storyline. Figure
1-1 illustrates the interrelated nature of these alternative scenario formulations.

Although no scenarios are value free, it is often useful to distinguish between
normative and descriptive scenarios. Normative (or prescriptive) scenarios are
explicitly values-based and teleologic, exploring the routes to desired or undesired
endpoints (utopias or dystopias). Descriptive scenarios are evolutionary and
open-ended, exploring paths into the future. The SRES scenarios are descriptive
and should not be construed as desirable or undesirable in their own right.
They are built as descriptions of possible, rather than preferred, developments.
They represent pertinent, plausible, alternative futures. Their pertinence is
derived from the need for policy makers and climate-change modelers to have
a basis for assessing the implications of future possible paths for GHG and
SO2 emissions, and the possible response strategies. Their plausibility is based
on an extensive review of the emissions scenarios available in the literature,
and has been tested by alternative modeling approaches, by peer review (including
the "open process" through the IPCC web site), and by the IPCC review and approval
processes. Good scenarios are challenging and court controversy, since not everybody
is comfortable with every scenario, but used intelligently they allow policies
and strategies to be designed in a more robust way.